experiment-loop

Multi-agent system experiment automation handles the full lifecycle from proposal and scenario sanity checks to full sweep execution. You define a hypothesis, and the workflow generates parameters, runs tests, and synthesizes results for review.

A sweep matrix in research workflow automation is a systematically generated set of parameter combinations for experiments. It allows you to test multiple variables across different scenarios automatically during a full run execution.

Scenario sanity checks validate experiment designs before full execution by testing parameters against expected constraints. This prevents wasted compute resources by ensuring scenarios are valid and properly configured before the full sweep.

Experiment lifecycle automation requires a Python environment and the swarm research framework. These dependencies provide the necessary infrastructure for scenario testing, sweep matrix generation, and result synthesis.

Experiment result synthesis automatically aggregates data from full run executions across multiple seeds and parameters. It compiles findings, updates research logs with next steps, and prepares the synthesized outcomes for human review.

Automated research log updates occur during the synthesis and review phase of the experiment lifecycle. The system records findings and proposed next steps directly into your research memory after completing the full run execution.